改进蚁群算法的机器人路径规划研究

针对传统蚁群算法存在算法收敛速度慢、易陷入局部最优的问题,文中提出了一种改进的蚁群算法。在传统A ^*算法的基础上,改进其估价函数,并将其引入到蚁群算法中,提出了改进启发函数η,增加目标点对路径搜索的吸引力,提高了收敛速度。新方法还改进了信息素挥发因子ρ,使信息素挥发因子处于动态变化,提高了算法的全局搜索能力,避免陷入局部最优。仿真结果表明,改进的蚁群算法在收敛速度上比传统蚁群算法提高了近50%,在最短路径上明显优于传统的蚁群算法,证明了改进算法的有效性。

Research on Robot Path Planning Based on Improved Ant Colony Algorithm

Aiming at the problem that the traditional ant colony algorithm had slow convergence speed and easy to fall into local optimum, an improved ant colony algorithm was proposed. Based on the traditional A ^* algorithm, the valuation function of the traditional A ^* algorithm was improved. which was further introduced into the ant colony algorithm. The modified heuristic function η was proposed to increase the attraction of the target point to the path search and improve the convergence speed. The pheromone volatilization factor ρ was improved, and the pheromone volatilization factor was dynamically changed, which promoted the global search ability of the algorithm and prevent it from falling into local optimum. The simulation results showed that the improved ant colony algorithm was nearly 50% faster than the traditional ant colony algorithm in convergence rate, and was superior to the traditional ant colony algorithm in the shortest path, which proved the effectiveness of the improved algorithm.